Papers with Arabic NLP tasks
Revisiting Common Assumptions about Arabic Dialects in NLP (2025.acl-long)
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| Challenge: | Existing assumptions about Arabic dialect variation are not quantitatively verified. |
| Approach: | They extend and analyze Arabic dialects to assess their validity using a multi-label dataset . they find that the assumptions oversimplify reality and are not always accurate . |
| Outcome: | The proposed methods oversimplify reality and are not always accurate, the authors argue . they show that the proposed assumptions oversimply represent reality and may hinder future work . |
Enhancing Arabic NLP Tasks through Character-Level Models and Data Augmentation (2025.coling-main)
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| Challenge: | Using character-level models, natural language processing for Arabic is challenging due to its rich morphology, root-based word formation, flexible sentence structures, diacritical ambiguities, and orthographic variations. |
| Approach: | They propose a character-level approach specifically designed for Arabic NLP tasks that incorporates Convolutional Neural Networks (CNNs), pre-trained transformers (CANINE), and Bidirectional Long Short-Term Memory networks (BiLSTMs). |
| Outcome: | The proposed model outperforms existing models on Arabic privacy policy classification task and reports a micro-averaged F1 score of 93.8%, surpassing state-of-the-art models. |
AraReasoner: Evaluating Reasoning-Based LLMs for Arabic NLP (2025.findings-emnlp)
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| Challenge: | Large language models have shown remarkable progress in reasoning abilities and general natural language processing tasks, yet their performance on Arabic data remains underexplored. |
| Approach: | They compare reasoning-focused LLMs with deepSeek models across 15 Arabic NLP tasks . they use zero-shot, few-shot and fine-tuning to evaluate their capacity for linguistic reasoning . |
| Outcome: | The proposed models outperform strong models on Arabic datasets and are compared with other models. |